Community health scoring tool

ABSTRACT

A system and method for scoring and comparing communities includes the development of community health measures by on combining and supplementing healthcare data and community data from numerous sources. The community health measures may be stored in a community health measures database and may be accessed by interactive tools to generate customized representations of healthcare measures for selected communities. The representations are automatically computed in response to interactive user selections. Geographic map representation, heat map representations and data tables may be automatically generated to identify correlations between health outcomes and population attributes in different communities.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the priority of U.S. Provisional Patent Application No. 62/028,627 entitled COMMUNITY HEALTH SCORING TOOL which was filed on Jul. 24, 2014 and which is incorporated herein by reference in its entirety. The present application also claims the priority of U.S. Provisional Patent Application No. 62/048,082 entitled COMMUNITY HEALTH SCORING TOOL which was filed on Sep. 9, 2014 and which is incorporated herein by reference in its entirety.

FIELD OF TECHNOLOGY

The present disclosure relates generally to health care information processing and particularly to systems and frameworks for measuring health care resource distribution.

BACKGROUND

A large amount of consumer healthcare information is routinely collected by healthcare providers, insurance providers, government agencies, researchers and other institutions. Even though much of the information is stored electronically, analyzing the information to improve health care delivery generally involves extensive efforts to identify appropriate data sources and to secure access to the data sources. Useful healthcare information may be stored on diverse computer networks and data storage systems, which may often be difficult or impossible to access for research purposes. Refining or expanding research efforts to analyze different parameters may often involve repeated efforts to access different data sources. Due to these and other difficulties, useful data representations that are suitable to support decisions for healthcare resource allocation in various communities have heretofore been scarce.

SUMMARY

A method for measuring community health care attributes according to an aspect of the present disclosure includes storing a first collection of health care data in one or more electronic storage systems. The first collection includes a number of health outcomes for health care consumers in a number of communities. The health care outcomes may include measures of health care cost, health care quality, and population health, for example.

A system for measuring health care related characteristics of a population segment according to another aspect of the present disclosure includes one or more electronic data storage systems coupled in electronic communication with one or more health care data sources. The system also includes a community health care database stored in one or more of the electronic data storage system(s) and one or more processors coupled in electronic communication with the electronic data storage systems. The community health care database includes a first collection of health care data. The first collection includes health outcomes for health care consumers in a number of communities, in which the health care outcomes for each consumer are associated with one or more of the communities. A second collection of community data is also stored in the electronic data storage system(s). The second collection includes population attributes that characterize the populations of healthcare consumers, resources, infrastructure and/or environment in each of the communities. The processor(s) are configured for receiving a first interactive input that selects one or more of the health care outcomes and/or one or more of the population attributes, identifying a correlation between the selected health care outcomes and one or more of the population attributes by accessing the first collection of health care data and the second collection of community data in response to receiving the interactive inputs, and representing the correlation to a user.

A method of measuring community health care characteristics according to another aspect of the present disclosure includes receiving one or more health care outcomes for each of a number of communities, automatically scoring each of the communities based on the corresponding health care outcomes and displaying a representation of the communities arranged based on their score.

A method for measuring community health care attributes, according to another aspect of the present disclosure includes storing a collection of health care data in one or more electronic storage systems. The collection of health care data includes a health outcomes for health care consumers in a number of communities and/or population attributes of the communities. The method includes associating the health care outcomes and/or population attributes for each consumer with one or more of the communities to generate a community health care database.

Additional features and advantages of the present disclosure are described below. It should be appreciated by those skilled in the art that this disclosure may be readily utilized as a basis for modifying or designing other structures, systems and processes for carrying out the same purposes of the present disclosure. It should also be realized by those skilled in the art that such equivalent implementations do not depart from the teachings of the disclosure as set forth in the appended claims. The novel features, which are believed to be characteristic of the disclosure, both as to its organization and method of operation, together with further objects and advantages, will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The particular features and advantages of the present disclosure will be apparent from the detailed description set forth below in conjunction with the drawings in which like reference characters identify corresponding aspects throughout.

FIG. 1 shows a block diagram of a data communication system according to aspects of the present disclosure.

FIG. 2 is a block diagram of a system that can implement part or all of one or more aspects or processes of systems to implement a community health measures tool according to embodiments of the present disclosure.

FIGS. 5A and 5B are illustrations of an interactive user interface including user controls for generating a geographical map representation of community health measures according to aspects of the present disclosure.

FIGS. 6A and 6B are geographical representations of community health measures generated according to aspects of the present disclosure.

FIGS. 7A and 7B are heat map representations of community health measures generated according to aspects of the present disclosure.

FIG. 8 is a process flow diagram illustrating a method for measuring community healthcare attributes according to aspects of the present disclosure.

FIG. 9 is a system block diagram illustrating a system for measuring health care related characteristics of a population segment according to aspects of the present disclosure.

DETAILED DESCRIPTION

The features and advantages of the present disclosure will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.

Computer systems may be coupled together in various ways to enable communications between them, including being coupled together in networks such as local area networks (LANs), wide area networks (WANs), or combinations of networks, such as the Internet and world wide web. Data may be transferred (e.g., copied or moved) between computer systems in various ways. For instance, an application executing at a first computer system may generate a query, which is a request for particular data. The query may be transmitted to a second computer system, which contains or has access to a data source containing the desired data. The second computer system responds to the query by transmitting the requested data to the first computer system.

The present specification discloses one or more embodiments that incorporate the features of the invention. The disclosed embodiment(s) merely exemplify the invention. The scope of the invention is not limited to the disclosed embodiment(s). The invention is defined by the claims appended hereto.

References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

Aspects of the present disclosure relate to data communications in distributed systems. For example, FIG. 1 shows a block diagram of a data communication system 100, according to an example embodiment. As shown in FIG. 1, system 100 includes a first computer system 102, a second computer system 104, a first storage 114, a network 116, and a second storage 118. An application 106 executes in first computer system 102. Storage 114 is coupled to first computer system 102. Storage 118 is coupled to second computer system 104. First and second computer systems 102 and 104 are communicatively coupled by network 116. System 100 is configured to enable resources to be transferred between first and second computer systems 102 and 104.

First and second computer systems 102 and 104 may each be any type of computing device, including a desktop computer (e.g., a personal computer), a server, a mobile computer or computing device such as a smart phone or tablet computer device, a personal digital assistant (PDA), a laptop computer, a notebook computer, etc., or other type of computer system. Storage 114 and storage 118 may each include one or more of any type of storage mechanism to store content (e.g., objects), including a hard disk drive, an optical disc drive, a memory device such as a RAM device, a ROM device, etc., and/or any other suitable type of storage medium.

Network 116 may include one or more communication links and/or communication networks, such as a LAN (local area network), a WAN (wide area network), or a combination of networks, such as the Internet. First and second communication links 122 and 124, which respectively couple first and second computer systems 102 and 104 to network 116, may include any number of communication links, including wired and/or wireless links, such as IEEE 802.11 wireless LAN (WLAN) wireless links, Worldwide Interoperability for Microwave Access (Wi-MAX) links, cellular network links, wireless personal area network (PAN) links (e.g., Bluetooth™ links), Ethernet links, USB links, etc.

Application 106 may issue a query for a resource (e.g., data). The resource may be accessible as data 108 contained in storage 118 at second computer system 104. To obtain the resource, first computer system 102 may transmit the query from first computer system 102 in a first communication signal 110. For example, first computer system 102 may contain an agent (e.g., a “client” agent) configured to handle transmission of queries. First communication signal 110 is transmitted through a first communication link 122, network 116, and a second communication link 124, and is received by second computer system 104. First communication signal 110 may be transmitted in any form, including in the form of a stream of packets (e.g., IP packets).

Second computer system 104 processes the request received in first communication signal 110. For example, second computer system 104 may include an agent (e.g., a “server” agent) configured to process received queries. Second computer system 104 retrieves data 108 from storage 118, which may contain a database or other data source. Second computer system 104 generates a second communication signal 112, which is a response signal that includes data 108. Second communication signal 112 is transmitted through second communication link 124, network 116, and first communication link 122, and is received by first computer system 102. Application 106 receives data 108 included in second communication signal 112, which may be stored in storage 114 (as indicated by dotted lines in FIG. 1). Second communication signal 112 may be transmitted in any form, including in the form of a stream of packets (e.g., IP packets).

Currently, applications and services are being developed that include the use of REST (representational state transfer) interfaces for accessing resources and a URI (Uniform Resource Identifier) namespace that identifies the resources. These applications and services enable web-based data sources to be accessed in a more efficient manner. For example, second computer system 104 in FIG. 1 may be configured to have a REST interface to enable data 108 to be accessed according a URI.

FIG. 2 is a block diagram of a system 200 that can implement part or all of one or more aspects or processes of systems within which a web-native bridge according to embodiments of the present disclosure can operate or within which methods according to embodiments of the present disclosure can be carried out. As shown in FIG. 2, memory 230 configures the processor 220 to implement one or more aspects of the methods, steps, and functions disclosed herein (collectively, shown as process 280 in FIG. 2). Different method steps can be performed by different processors. The memory 230 could be distributed or local and the processor 220 could be distributed or singular. The memory 230 could be implemented as an electrical, magnetic or optical memory, or any combination of these or other types of storage devices. It should be noted that if distributed processors are employed, each distributed processor that makes up processor 220 generally contains its own addressable memory space. It should also be noted that some or all of computer system 200 can be incorporated into an application-specific or general-use integrated circuit. For example, one or more method steps could be implemented in hardware in an ASIC rather than using firmware. Display 240 is representative of a variety of possible input/output devices (e.g., displays, touchscreens, mice, keyboards, and so on).

A system for measuring health care related characteristics of a population segment according to an aspect of the present disclosure includes one or more electronic data storage systems coupled in electronic communication to one or more health care data sources. The electronic data storage systems may be coupled to the health care data sources directly or indirectly by one or more different means for communication such as direct wiring, wireless communication, fiber optics, and may involve communication via one or more intermediate communication network such as the Internet, for example. The health care data sources may include various entities, groups or networks that are involved with the healthcare industry and which generate, receive and/or collect healthcare related information. Healthcare data sources may include healthcare providers, healthcare payers, researchers and/or government agencies, for example.

Because a large amount of healthcare data may at times be subject various strict privacy policies, regulations or statutes governing the storage and communication private healthcare information, aspects of the present disclosure include systems that may not be configured in a standard technical environment using only standard communication techniques, conventional general purpose computer networks and communication equipment, for example. Rather, aspects of the present disclosure may provide substantial improvements to the conventional technical environments for accessing, storing and/or communicating healthcare information include special purpose computer hardware, software, algorithms and/or communication techniques to de-identify data and/or to ensure that healthcare data is accessed, communicated and stored in a manner that private healthcare data may include private health information. For example, the system may include special purpose computer hardware, software, algorithms and/or communication techniques or portions thereof that may be implemented by only a small number of healthcare industry stakeholders such as healthcare payer and provider networks to ensure data privacy.

According to aspects of the present disclosure, the system includes a community health care database stored in one or more of the electronic data storage system(s) and one or more processors coupled in electronic communication with the electronic data storage systems. In this implementation, the community health care database includes a first collection of health care data. The first collection includes health outcomes for health care consumers in a number of communities, in which the health care outcomes for each consumer are associated with one or more of the communities. A second collection of community data is also stored in the electronic data storage system(s). The second collection includes population attributes that characterize the populations of healthcare consumers, resources, infrastructure and/or environment in each of the communities. The processor(s) are configured for receiving a first interactive input that selects one or more of the health care outcomes and/or one or more of the population attributes, identifying a correlation between the selected health care outcomes and one or more of the population attributes by accessing the first collection of health care data and the second collection of community data in response to receiving the interactive inputs, and representing the correlation to a user.

According to an aspect of the present disclosure a database of previously unavailable community healthcare measures is generated by accessing sources of electronic healthcare data via communication links to one or more healthcare networks, for example, processing a wide variety of the healthcare data to compute population health outcomes for numerous communities and combing the population health outcomes with community data from numerous data sources.

The community data describes a large number of communities in terms of various population attributes. The population attributes include demographic population characteristics of each community as well as healthcare delivery system attributes for each community. The healthcare delivery system attributes may characterize system of health care, healthcare infrastructure and environment, for example. Examples of various population attributes that may be used to generate the disclosed healthcare measures database according to aspects of the present disclosure are listed in Table 1.

TABLE 1 Population characteristics Delivery System Attributes Social Capital Capacity Predicted mean health literacy Primary care providers/100,000 % with basic health literacy or above Specialis MDs/100,000 % high school graduates Hospital beds/1,000 Petris social capital Ambulatory centers/100,000 Per capita 501c3 revenues - regional non-profit Nurses/1000 activity PAs/1000 Aggregate social capital - emotional support Incentives % college graduates % hospitals with salaried physician Economics arrangements Median household income Change in income % hospital revenues from capitations 2000 to 2009 Unemployment rate Managed care penetration Employers and employment % hospital revenues at risk Demographics Integration and Alignment Race and ethnicity (1% distribution) % % hospitals with CPHO physician arrangements population living in rural area % hospitals with OPHO physician arrangements Health behaviors: % physicians working in hospital owned practices Smoking rate % physicians working in health system owned Chlamydia infections/1,000 practices Average life expectancy % physicians working in large (50+) practices Obesity rate % physicians in solo practice % physicians in IPAs % hospitals with IPAs ACO & PCMH coverage HIT % hospitals with electronic health records % hospitals with health information exchanges % hospitals with EMRs achieving MU % primary care providers receiving HIT incentive$

Examples of various healthcare outcomes that may be used to generate the disclosed healthcare measures database according to aspects of the present disclosure are listed in Table 2.

TABLE 2 Preference-Sensitive (PS) Care: “choosing Appropriate Care wisely” Adherence to clinical guidelines - appropriate PS cholecystectomy procedures/1,000 prescribing PS back surgery procedures/1,000 Adherence to clinical guidelines - Physician PS hysterectomy for uterine fibroid procedures/ follow up 1,000 Adherence to clinical guidelines - pregnancy lab PS lower extremity bypass procedures/1,000 screening PS carotid endarterectomy procedures/1,000 Adherence to clinical guidelines - testing PS angiography procedures/1,000 medication complications PS CABG procedures/1,000 Adherence to clinical guidelines - screening PS high tech diagnostic imaging/1,000 chronic conditions PS prostatectomy procedures/1,000 Medication Adherence (commercial) PS AAA procedures/1,000 Asthma medication adherence PS pacemaker insertion procedures/1,000 Depression medication adherence PS PCI/PTCA procedures/1,000 CAD medication adherence PS bone marrow/organ transplants/1,000 Diabetes medication adherence PS C-section rate Hyperlipidemia medication adherence PS mastectomy/1,000 Hypertension medication adherence PS total knee replacement procedures/1,000 Costs of Care - total and by site, type PS mitral-aortic valve procedures/1,000 Commercial costs - adjusted for age, gender, PS total shoulder replacement/1,000 and input prices PS total hip replacement/1,000 Medicare costs adjusted for age, gender, and Medicare preference sensitive knee risk replacements/1,000 Prevalence of disease Medicare preference sensitive hip Prevalence depression/1,000 replacements/1,000 Prevalence of lower back problems/1,000 Medicare preference sensitive back surgery/ Prevalence of migraine 1,000 Prevalence of COPD/1,000 Avoidable Care Prevalence of hypertension/1,000 Readmissions rate - commercial Prevalence of diabetes/1,000 Avoidable admissions/1,000, commercial Prevalence of CHF/1,000 Medicare avoidable admissions Prevalence of ESRD/1,000 Medicare readmission rate Prevalence of CAD/1,000 Avoidable ED visits/1,000 Utilization ED visits/1,000 Office visits/1,000 Inpatient admissions

According to aspects of the present disclosure, a community may include a geographical area, a demographic group within a population, a group of healthcare providers or other stakeholders, for example.

The community health measures include measures of health care cost, healthcare quality and community characteristics. Examples of the community characteristic that are combined with healthcare data to generate community health measures according to aspects of the present disclosure include socioeconomic status, economic activity, economic growth, social capital, structure and capacity of healthcare delivery systems, alignment of healthcare providers, incentives for healthcare stakeholders and adoption of healthcare information technology (HIT).

According to the present disclosure, the community healthcare measures are constructed based on data received from a number of public sources and proprietary sources. In one example, data received from public sources includes socioeconomic status (SES), economics, social capital information that is extracted from census and from the American Community Survey performed by the United States Census Bureau. According to aspects of the present disclosure, measures of health outcomes and healthcare costs are computed based on claims data that is electronically compiled by insurance providers such as United HealthCare of Minnetonka, Minn. Other measures of healthcare outcomes and costs may be computed based on data received from healthcare provider networks and medical associations such as the American Hospital Association, for example. Additional data for computing health outcomes and healthcare costs may be received from primary data collection performed by accountable care organizations in the community, for example. According to aspects of the present disclosure, data from any one or combination of these public and private data sources may be accessed to generate various health care measures.

The community health measures database that is generated according to aspects of the present disclosure may be used by stakeholders and policy makers in government and throughout the healthcare industry to improve healthcare delivery by increasing efficiency of healthcare networks, improving allocation of healthcare resources, and enhancing development, testing and deployment of healthcare innovations.

According to aspects of the present disclosure, the disclosed community health measures database is accessed to define and compute numerous new community health measures including: a community health measure that characterizes the structure of the delivery system; a community health measure that characterize supply of medical providers; a community health measure that characterize innovations in an organization of care; a community health measure that characterizes HIT adoption; a community health measure that characterizes health care costs; a community health measure that characterizes resource utilization; and a community health measure that characterizes appropriate care in the commercial population based on analysis of health insurance data, for example.

According to another aspect of the present disclosure, the community health measures database is incorporated in and/or accessed by a community health care measures tool. The community health measures tool leverages aspects of the community health measure database to generate previously unavailable representations of community health information. A community health measures tool according to aspects of the present disclosure may be implemented on personal computers, computer networks and/or mobile devices, for example.

One implementation of a community health measures tool according to aspects of the present disclosure includes a community measures scoring tool. The community healthcare scoring tool may be configured to score communities based on their attainment of desirable health outcomes and/or based on availability of healthcare resources that attribute to desirable health outcomes, for example. Performance scores and ranking of the communities is computed by accessing and statistically processing selected population attributes of the communities in the community health measures database.

According to an aspect of the present disclosure user-defined weights for particular population attributes and/or healthcare outcomes may be predefined or received by interactive input to customize the rankings according to user needs and criteria. In this implementation, users may define the weights to customize the computation and/or representation of community rankings. The community scores, rankings, and comparative similarity with respect to selected health outcomes and population attributes may be interactively computed to support a wide range of a number of business and clinical decisions.

A community health measures tool according to an aspect of the present disclosure includes a computer implemented application configured with a graphical user interface for receiving a selection and weighting of communities, population attributes and/or healthcare outcomes, and displaying a representation of the selected communities, population attributes and/or healthcare outcomes. The representation is computed and displayed by processing data in a healthcare measures database based on the selections and weightings of communities, population attributes and/or healthcare outcomes.

In one example, the community measures scoring tool is configured to represent community efficiency scores by receiving and/or computing composite healthcare input measures and composite health outcomes for each of a number of communities based on the population attributes and health outcomes in the community health measures database. The composite healthcare input measures and composite health outcome measures are statistically processed to generate scores that represents an efficiency measure for each respective community.

According to aspects of the present disclosure, selected community scores may be juxtaposed in a map display, represented in a table, or arranged in a heat map to identify similar communities, for example. A map display according to aspects of the present disclosure may be color coded or shaded to represent selected community scores with respect to selected health outcomes and/or community attributes. In one example, the tool may be configured to display a color of a community on a map to represent a number of standard deviations of the corresponding community's score from a mean community score in a selected health care measure. In another example, the tool may be configured to display a color of each community on a map to represent the percentile or decile of scores in all communities in which the respective communities scored with respect to a particular healthcare measure, for example. In table display according to aspect of the present disclosure may display a list of communities ranked by their score in a selected health care measure.

Alternatively the community measures scoring tool may be configured to display a heat map representation of community scores. The heat map representation displays a color coded or shaded grid in which colors and/or shading of grid elements represent relative scores attained by each of a number of selected communities in each of a number of selected health outcomes and/or population attributes. According to another aspect of the present disclosure, a clustering process may be used to cluster together certain healthcare outcomes and/or population attributes. The clustering process may be used to generate a heat map representation, which identifies factors that act similarly in a particular group of communities, and which identifies communities that are most similar to each other, for example.

Implementations of the disclosed community measures scoring tool may be used to identify communities that are similar or dissimilar with respect to selected healthcare outcomes and/or selected population attributes in the community health measures database. The community measures scoring tool may be configured to represent similar or dissimilar communities by juxtaposing geographical representations of scores for corresponding communities on a map display, and/or by displaying a table of similar and dissimilar communities, for example. Degrees of similarity or dissimilarity between a selected community and other communities may be represented by different colors and/or different shading on a map display, or may be indicated as numerical scores in a table, for example. This allows users to select a community, and determine what are the other communities that are most similar or least similar to the selected community in terms of a range of health outcomes and/or population attributes.

In one example according to an aspect of the present disclosure, a clinical translation and trial tool is configured to represent community similarity scores to identify appropriate locations for piloting or testing medical devices, drugs or other innovations. Favorable locations for piloting or testing a new drug or health care innovation may be chosen based on their likelihood of furnishing a sufficiently large test population that is correlated to one or more selected attributes, for example. The clinical translation and trial tool also helps stakeholders chose favorable locations for implementing innovations based on favorable results of piloting or testing the innovation in a similar community.

The tools and methods described herein enable healthcare stakeholders such as communities, government agencies, insurance companies, health care networks and/or health care plans to make appropriate decisions to increase health care value. The tools and methods described herein may be used to evaluate stakeholder performance with respect to certain actionable aspects of affecting cost and health care use compared and compare the stakeholder's performance to certain benchmarks or to the performance of other stakeholders, for example. By quantifying the performance of stakeholders relative to a particular market segment, the stakeholders may improve assessments of their own market performance versus market potential, for example.

The tools and methods described herein may also be used by healthcare stakeholders to evaluate how efficiently they are using available resources to generate favorable healthcare outcomes. This allows stakeholders to identify gaps in the distribution of key resources, improve resource allocation and identify actions that may be limiting their performance.

According to an aspect of the present disclosure, the community health measures tool allows a user to select healthcare outcomes and/or population attributes, and automatically displays a representation of corresponding community healthcare measures that allow the user to easily recognize communities that stand out in terms of the selected healthcare outcomes and/or population attributes. The community health measures tool also allows a user to select particular communities so that measures of corresponding healthcare outcomes and population attributes of the communities can be automatically displayed for comparison on a geographical map, a table, and/or a heat map, for example.

An example implementation of an interactive community health measure tool for identifying similar communities in terms of health care outcomes and population attributes according to an aspect of the present disclosure is described with reference to FIG. 5A. The system includes one or more user input fields, such as lists, check boxes and sliders to receive interactive input from a user and generates a representation of communities having similar or dissimilar community health measures based on the interactive input. In the example shown in FIG. 5A, a city selection input field 502 and a state selection input field 504 allow a user to easily select a city and state as a base community for comparing with other communities. A comparison basis selection input field 506 allows a user to select whether health care outcomes or inputs or both will be used as a basis of comparison for computing degrees of community similarity. The inputs may include population attributes that drive health care cost and quality in a community, for example. Population attributes that may be used as inputs for comparison include health behaviors, provider supply, social capital economic performance, provider incentives, HIT adoption and provider integration, for example. According to an aspect of the present disclosure one or more sliders 508 are provided to receive a weight selection for one or more of the population attributes to be compared. This allows a user to interactively assign greater weight to more important population attributes and to assign less weight to less important population attributes for generating the comparison and identifying communities similar to the selected base community.

According to an aspect of the present disclosure, a representation of comparative communities 510 is automatically generated and displayed based on the parameters selected in the input fields. The representation 510 automatically updates in response to changes in any of the input fields. The representation may include a geographic map display, a data table or a heat map, for example. According to an aspect of the present disclosure, a display selection input field 512 allows a user to select which type of representation to be displayed. In this example, the representation 510 shows that the communities highlighted in one color are most similar to the selected community of Los Angeles in terms of the inputs that contribute to a health care result. The communities that are highlighted in another color are the least similar to Los Angeles in terms of the same inputs.

Another example implementation of an interactive community health measures tool for identifying communities based on a selected health measure is described with reference to FIG. 5B. An aggregate measures selection input field 514 and an individual measures selection input field 516 allow a user to easily select a healthcare measures as a basis for comparing communities on a representation 518. In this example the selected aggregate measure is a measure of preference sensitive procedures and the selected individual measure is a measure of high tech diagnostic imaging. A display limit field 520 allows a user to select a number of communities to be represented on the representation 518. A region selection input field 522 and a geographic area type selection field 524 allow a user to select the geographical region to be displayed. The selection of displayed regions may be further refined by a hospital referral region (HRR) selection field 525.

According to an aspect of the present disclosure, a representation of the selected healthcare measures is automatically generated and displayed in a map representation 510 of the selected regions. The representation of the selected healthcare measures may include a color coding and/or shading gradient of regions on the map representation based on a level of the selected measure. A measures key 526 displays an association of the shading or color coding of displayed regions with a corresponding level or range of levels of the selected measure. In this example, the levels are percentiles of the selected measure. The levels may displayed in order of favorable levels or unfavorable levels based on an order selection input field 528. A load data button 530 and a clear data button 532 allows users to clear the representation 518 and change the displayed representation 518 based upon different input selections.

An example implementation of an interactive community health measure tool to generate geographical map representations of healthcare measures according to aspects of the present disclosure is described with reference to FIGS. 6A and 6B in which the displayed measures indicate comparative population Medicare costs and commercial healthcare costs. Referring to FIG. 6A a first geographical map representation 602 indicates communities that have relatively low population Medicare costs. The first geographical map representation 602 is juxtaposed with a second geographical map representation 604 showing communities that have relatively high population Medicare costs. Referring to FIG. 6B a third geographical map representation 606 displays communities that have relatively low population commercial medical costs. The third geographical map representation 606 is juxtaposed with fourth geographical map representation 608 showing communities that have relatively high population commercial costs. The comparative costs are displayed by color coding the communities based on their standard deviation from mean community costs. These geographical map representation allows health care policy makers and other stakeholders to identify comparative high performing communities and comparatively low performing communities for analyzing equity of resource distribution, identifying where certain opportunities or needs exist, and/or identifying where certain policies have a particular level of effect, for example.

The community health measures tool may be implemented to quickly generate custom geographical map representations of health care measures by interactively selecting parameters for comparison from a vast database of community healthcare measures. Examples of other geographic map representations of community health measures generated according to aspects of the present disclosure include geographic map representations of medication adherence, health system integration and technology adoption, avoidable hospitalizations, prevalence of smokers and prevalence of particular illness, such as emphysema, for example.

An example implementation of an interactive community health measure tool to generate heat map representations of healthcare measures according to aspects of the present disclosure is described with reference to FIGS. 7A and 7B. Referring to FIG. 7A, a first heat map representation 702 includes an array of grid elements that are each color coded to represent a scores for one of a number population attributes and health outcomes for each of a number of corresponding communities. In this example, a first heat map representation 702 includes communities 704 that are identified, using the community health measures tool, as the fifty communities represented in a community health measures database as having the lowest Medicare total costs. The displayed population attributes 706 and health outcomes include measures of Medicare costs and measure of commercial medical costs, for example.

Referring to FIG. 7B, a second heat map representation 708 includes communities 710 that are identified, using the community health measures tool, as the fifty communities represented in the community health measures database as having the lowest commercial medical costs. The displayed population attributes 712 and health outcomes include measures of Medicare costs and measure of commercial medical costs, for example. By analyzing the first heat map 702 and the second heat map 708, it may be observed that communities having low Medicare costs often have high commercial costs and vice versa.

According to aspects of the present disclosure, the heat map functionality of the community health measures tool allows users to interactively cluster together attributes/factors and identify which attributes/factors that act similarly in a particular group of communities, and to identify which communities are most similar to each other, for example relative to other communities. The resulting heat map representations can be used to identify patterns of data that are otherwise substantially undetectable.

To assist pattern recognition, various clustering techniques may be applied to the information displayed in a heat map representation. In FIGS. 7A and 7B, brackets are displayed at the periphery of each heat map representation 702, 708 to indicate groupings of the displayed communities 704, 710 and groupings of the displayed population attributes and health outcomes 706, 702. In this example, the brackets are automatically generated using a grouping algorithm. The informative clustering patterns can be useful for improved hypothesis generation and addressing disparities between communities, for example.

Clustering can be performed based on any number of the attributes in the Community Health Measures Project database. Examples of other heat map representations of community health measures generated according to aspects of the present disclosure include representations different communities in terms of ethnicity and income, for example.

Geographic map representations generated by the community health measures tool may often be used together with heat map representations generated by the community health measures tool according to aspects of the present disclosure. In some implementation the complementary geographic map representations and heat map representations may provide a more robust comparison of communities.

A method for measuring community health care attributes according to an aspect of the present disclosure is described with reference to FIG. 8. The method 800 includes storing a first collection of health care data in one or more electronic storage systems at block 802. The first collection includes a number of health outcomes for health care consumers in a number of communities. The health care outcomes may include measures of health care cost, health care quality, and population health, for example.

At block 804, the method includes associating the health care outcomes for each consumer with one or more of the communities to generate a community health care database. At block 806, the method includes storing a second collection of community data in the one or more electronic data storage systems. The second collection includes a number of population attributes that characterize the health care consumers in each of the communities. The second collection may also include a number of population attributes that characterizes health care providers in each of the communities. The population attributes may include measure of social capital, economics, demographics, health behaviors, health care provider capacity, health care provider incentives, integration and alignment of health care providers, and health information technology, for example.

At block 808, the method includes identifying a correlation between one or more of the health care outcomes and one or more of the population attributes by accessing the first collection of health care data and the second collection of community data. At block 810, the method includes representing the correlation to a user.

The method 800 for measuring community health care attributes may also include assigning an outcome rank to each of the health care outcomes based on a predetermined hierarchy of outcomes to generate an enhanced community health care database, and representing one or more of the communities in association with one or more corresponding outcome ranks assigned to a respective healthcare outcome in the respective community.

According to another aspect of the present disclosure, the method 800 may also include assigning a population attribute rank to each of the population attributes, and representing one or more of the communities based on the ranking of one or more of the population attributes associated with the respective communities. The method 800 may also include computing a correlation between selected health care outcomes with selected population attributes and identifying similar communities based on a result of the correlating.

According to another aspect of the present disclosure, the method 800 may also include receiving an input that selects one or more of the population attributes and/or one or more of the health care outcomes, and interactively computing the correlation in response to receiving the input.

A system for measuring health care related characteristics of a population segment according to an aspect of the present disclosure is described with reference to FIG. 9. The system 900 includes one or more electronic data storage systems 902 coupled to one or more health care networks 904. A community health care database is stored in the electronic data storage system(s) 904. The community health care database including a first collection of health care data 906. The first collection of health care data 906 includes a number of health outcomes for health care consumers in a number of communities. The health care outcomes for each consumer are associated with one or more of the communities. A second collection of community data 908 is also stored in one or more of the electronic data storage systems 904. The second collection of community data 908 includes a number of population attributes that characterize the health care consumers in each of the communities. One or more processors 910 are coupled in electronic communication with the electronic data storage systems 904.

According to aspects of the present disclosure the processor(s) 910 are configured for receiving a first interactive input that selects one or more of the health care outcomes and/or one or more of the population attributes. The health care outcomes may include measures of health care cost, health care quality, and population health, for example. The population attributes may include measure of consisting of social capital, economics, demographics, health behaviors, health care provider capacity, health care provider incentives, integration and alignment of health care providers, and health information technology, for example.

The processor(s) 910 may also be configured for identifying a correlation between the selected health care outcomes and one or more of the population attributes by accessing the first collection of health care data and the second collection of community data in response to receiving the interactive inputs, and representing the correlation to a user. According to an aspect of the present disclosure, the processor(s) 910 may be configured to interactively compute the correlation in response to receiving the input.

According to an aspect of the present disclosure, the processor(s) 910 may also be configured to assign a population attribute rank to each of the population attributes, and represent one or more of the communities based on the ranking of one or more of the population attributes associated with the respective communities. The processor(s) 910 may also be configured to assign an outcome rank to each of the health care outcomes based on a predetermined hierarchy of outcomes to generate an enhanced community health care database and represent one or more of the communities in association with one or more corresponding outcome ranks assigned to a respective healthcare outcome in the respective community. According to another aspect of the present disclosure, the processor(s) 910 may be configured to identify similar communities based on a result of the correlating.

Another aspect of the present disclosure includes a method of measuring community health care characteristics. According to this aspect, the method includes receiving one or more health care outcomes for each of a number of communities, automatically scoring each of the communities based on the corresponding health care outcomes, and displaying a representation of the communities arranged based on their score. The method may also include receiving one or more population attributes that characterize the health care consumers in each of the communities, and displaying in indication of the corresponding population attributes in the representation of the communities.

According to one aspect of the present disclosure, the method includes receiving an input that selects one or more of the population attributes and one or more of the health care outcomes; and interactively displaying a geographical representation of a relationship between the selected population attributes and the selected health care outcomes in response to receiving the input. According to another aspect of the present disclosure, the method includes receiving an input that selects a number of the population attributes, and interactively displaying a heat map representation of health care measures based on the selected population attributes for each of a number of communities in response to receiving the input. The method may also include generating clustering information defining relationships between the displayed health care measures and rearranging the heat map representation based on the clustering information.

A method for measuring community health care attributes according to another aspect of the present disclosure includes storing a first collection of health care data in one or more electronic storage systems and associating the health care outcomes for each consumer with one or more of the communities to generate a community health care database. The first collection includes a number of health outcomes for health care consumers in a number of communities. The method may also include identifying a correlation between a first one of the health care outcomes associated with a community and a second one of the health care outcomes associated with the community. The method then includes representing the correlation to a user.

According to an aspect of the present disclosure, the method for measuring community health care attributes may also include storing a second collection of community data in the one or more electronic data storage systems. The second collection may include a number of population attributes that characterize the health care consumers in each of the communities and/or a number of population attributes that characterizes health care providers in each of the communities, for example. According to one aspect of the present disclosure, the method includes identifying a correlation between a first one of the population attributes and a second one of the population attributes and representing the correlation to a user. According to another aspect of the present disclosure, the method includes identifying a correlation between one or more of the health care outcomes and one or more of the population attributes by accessing the first collection of health care data and the second collection of community data and representing the correlation to a user.

The method may also include statistically processing the health care outcomes to generate a number of categories of health care outcomes and/or statistically processing the population attributes to generate a number of categories of population attributes. According to one aspect of the present disclosure, the health care outcomes are grouped into a number of categories, including measures of health care cost, measures of health care quality, and measures of population health, for example. According to another aspect of the present disclosure, the population attributes are grouped into a number of categories including measure of social capital, measures of economics, measures of demographics, measures of health behaviors, measures of health care provider capacity, measures of health care provider incentives, measures of integration of health care providers, measures of alignment of health care providers, and measures of health information technology, for example.

The method for measuring community health care attributes may also include assigning an outcome score to each of the health care outcomes based on a predetermined hierarchy of outcomes and computing a composite outcome score for each community by statistically combining the outcome scores in each community respective community. The method may also include representing one or more of the communities based a respective composite outcome score of the respective communities.

According to another aspect of the present disclosure, the method includes assigning a population attribute score to each of the population attributes, and computing a composite population attribute score for each community by statistically combining the population attribute scores in each respective community. The method may also include representing one or more of the communities based on the composite population attribute score of the respective communities. The method may also include computing a community efficiency score by statistically comparing the composite population attribute score of the community with the composite outcome score of the community. For example, the method may include computing community efficiency scores for each of a number of communities by statistically comparing the composite population attribute score of each of the communities with the composite outcome score of each respective one of the communities and displaying the community efficiency scores for each of the communities.

According to an aspect of the present disclosure, the method includes displaying a geographical representation of a relationship between selected health outcomes and/or selected population attributes. According to another aspect of the present disclosure, the method includes displaying a heat map representation of health care measures based on selected health care outcomes and/or selected population attributes. The heat map representation includes a composite outcome score indication and/or a composite population attribute score indication for each of a number of the communities. The method may also include computing clustering information that defines relationships between the displayed health care measures and rearranging the heat map representation based on the clustering information.

A method for measuring community health care attributes according to another aspect of the present disclosure includes storing a first collection of community data in the one or more electronic data storage systems. The first collection including a number attributes that characterize health care consumers in each of a number of communities. The method also includes associating the attributes that characterize the health care consumers with one or more of the communities to generate a health care community population attribute database, identifying a correlation between a first one of the attributes in a community and a second one of the attributes in the community by accessing the health care community population attribute database and representing the correlation to a user.

The terms “computer program medium” and “computer usable medium” are used to generally refer to media such a as removable storage drive and a hard disk installed in a hard disk drive. These computer program products provide software to a computer system.

Computer programs (also referred to as computer control logic) are stored in main memory and/or secondary memory. Computer programs may also be received via communications interface. Such computer programs, when executed, enable the computer system to perform the features as discussed herein. In particular, the computer programs, when executed, enable the processor to perform the features of various embodiments. Accordingly, such computer programs represent controllers of the computer system.

In various embodiments, software may be stored in a computer program product and loaded into a computer system using removable storage drive, hard disk drive or communications interface. The control logic (software), when executed by the processor, causes the processor to perform the functions of various embodiments as described herein. In various embodiments, software may be implemented in hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine so as to perform the functions described herein will be apparent to persons skilled in the relevant art(s).

The system contemplates uses in association with web services, utility computing, pervasive and individualized computing, security and identity solutions, autonomic computing, cloud computing, commodity computing, mobility and wireless solutions, open source, biometrics, grid computing and/or mesh computing.

Databases discussed herein may include relational, hierarchical, graphical, or object-oriented structure and/or any other database configurations. Common database products that may be used to implement the databases include DB2 by IBM (Armonk, N.Y.), various database products available from Oracle Corporation (Redwood Shores, Calif.), Microsoft Access or Microsoft SQL Server by Microsoft Corporation (Redmond, Wash.), MySQL by MySQL AB (Uppsala, Sweden), or any other suitable database product. Moreover, the databases may be organized in any suitable manner, for example, as data tables or lookup tables. Each record may be a single file, a series of files, a linked series of data fields or any other data structure. Association of certain data may be accomplished through any desired data association technique such as those known or practiced in the art. For example, the association may be accomplished either manually or automatically. Automatic association techniques may include, for example, a database search, a database merge, GREP, AGREP, SQL, using a key field in the tables to speed searches, sequential searches through all the tables and files, sorting records in the file according to a known order to simplify lookup, and/or the like. The association step may be accomplished by a database merge function, for example, using a “key field” in pre-selected databases or data sectors. Various database tuning steps are contemplated to optimize database performance. For example, frequently used files such as indexes may be placed on separate file systems to reduce In/Out (“I/O”) bottlenecks.

One skilled in the art will also appreciate that, for security reasons, any databases, systems, devices, servers or other components of the system may consist of any combination thereof at a single location or at multiple locations, wherein each database or system includes any of various suitable security features, such as firewalls, access codes, encryption, decryption, compression, decompression, and/or the like.

The computers discussed herein may provide a suitable website or other Internet-based graphical user interface which is accessible by users. In various embodiments, the Microsoft Internet Information Server (IIS), Microsoft Transaction Server (MTS), and Microsoft SQL Server, are used in conjunction with the Microsoft operating system, Microsoft NT web server software, a Microsoft SQL Server database system, and a Microsoft Commerce Server. Additionally, components such as Access or Microsoft SQL Server, Oracle, Sybase, Informix MySQL, Interbase, etc., may be used to provide an Active Data Object (ADO) compliant database management system. In various embodiments, the Apache web server is used in conjunction with a Linux operating system, a MySQL database, and the PHP, and/or Python programming languages.

Any of the communications, inputs, storage, databases or displays discussed herein may be facilitated through a website having web pages. The term “web page” as it is used herein is not meant to limit the type of documents and applications that might be used to interact with the user. For example, a typical website might include, in addition to standard HTML documents, various forms, Java applets, JavaScript, active server pages (ASP), common gateway interface scripts (CGI), extensible markup language (XML), dynamic HTML, cascading style sheets (CSS), AJAX (Asynchronous Javascript And XML), helper applications, plug-ins, and the like. A server may include a web service that receives a request from a web server, the request including a URL (http://yahoo.com/stockquotes/ge) and an IP address (123.56.789.234). The web server retrieves the appropriate web pages and sends the data or applications for the web pages to the IP address. Web services are applications that are capable of interacting with other applications over a communications means, such as the Internet. Web services are typically based on standards or protocols such as XML, SOAP, AJAX, WSDL and UDDI, Web services methods are well known in the art, and are covered in many standard texts. See, e.g., ALEX NGHIEM, IT WEB SERVICES: A ROADMAP FOR THE ENTERPRISE (2003), hereby incorporated by reference.

Middleware may include any hardware and/or software suitably configured to facilitate communications and/or process transactions between disparate computing systems. Middleware components are commercially available and known in the art. Middleware may be implemented through commercially available hardware and/or software, through custom hardware and/or software components, or through a combination thereof. Middleware may reside in a variety of configurations and may exist as a standalone system or may be a software component residing on the Internet server. Middleware may be configured to process transactions between the various components of an application server and any number of internal or external systems for any of the purposes disclosed herein. WebSphere MQ™ (formerly MQSeries) by IBM, Inc. (Armonk, N.Y.) is an example of a commercially available middleware product. An Enterprise Service Bus (“ESB”) application is another example of middleware.

As will be appreciated by one of ordinary skill in the art, the system may be embodied as a customization of an existing system, an add-on product, a processing apparatus executing upgraded software, a stand-alone system, a distributed system, a method, a data processing system, a device for data processing, and/or a computer program product. Accordingly, any portion of the system or a module may take the form of a processing apparatus executing code, an internet based embodiment, an entirely hardware embodiment, or an embodiment combining aspects of the internet, software and hardware. Furthermore, the system may take the form of a computer program product on a computer-readable storage medium having computer-readable program code means embodied in the storage medium. Any suitable computer-readable storage medium may be utilized, including hard disks, CD-ROM, optical storage devices, magnetic storage devices, and/or the like.

The system and method is described herein with reference to screen shots, block diagrams and flowchart illustrations of methods, apparatus (e.g., systems), and computer program products according to various embodiments. It will be understood that each functional block of the block diagrams and the flowchart illustrations, and combinations of functional blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by computer program instructions.

These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions that execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block or blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchart illustrations support combinations of means for performing the specified functions, combinations of steps for performing the specified functions, and program instruction means for performing the specified functions. It will also be understood that each functional block of the block diagrams and flowchart illustrations, and combinations of functional blocks in the block diagrams and flowchart illustrations, can be implemented by either special purpose hardware-based computer systems which perform the specified functions or steps, or suitable combinations of special purpose hardware and computer instructions. Further, illustrations of the process flows and the descriptions thereof may make reference to user windows, webpages, websites, web forms, prompts, etc. Practitioners will appreciate that the illustrated steps described herein may comprise in any number of configurations including the use of windows, webpages, web forms, popup windows, prompts and the like. It should be further appreciated that the multiple steps as illustrated and described may be combined into single webpages and/or windows but have been expanded for the sake of simplicity. In other cases, steps illustrated and described as single process steps may be separated into multiple webpages and/or windows but have been combined for simplicity.

Benefits, other advantages, and solutions to problems have been described herein with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any elements that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as critical, required, or essential features or elements of the disclosure. The scope of the disclosure is accordingly to be limited by nothing other than the appended claims, in which reference to an element in the singular is not intended to mean “one and only one” unless explicitly so stated, but rather “one or more”.

Although illustrative embodiments of the present disclosure have been described herein with reference to the accompanying drawings, it is to be understood that the present disclosure is not limited to those precise embodiments, and that various other changes and modifications may be made by one skilled in the art without departing from the scope or spirit of the disclosure. 

What is claimed is:
 1. A method for measuring community health care attributes, comprising: storing a first collection of health care data in one or more electronic storage systems, the first collection including a plurality of health outcomes for health care consumers in a plurality of communities; associating the health care outcomes for each consumer with one or more of the communities to generate a community health care database; storing a second collection of community data in the one or more electronic data storage systems, the second collection including a plurality of population attributes that characterize the health care consumers in each of the communities; identifying a correlation between one or more of the health care outcomes and one or more of the population attributes by accessing the first collection of health care data and the second collection of community data; and representing the correlation to a user.
 2. The method of claim 1, wherein the second collection further includes a plurality of population attributes that characterizes a system of healthcare, a healthcare infrastructure, and/or a healthcare environment in each of the communities.
 3. The method of claim 1, further comprising: assigning an outcome rank to each of the health care outcomes based on a predetermined hierarchy of outcomes to generate an enhanced community health care database; and representing one or more of the communities in association with one or more corresponding outcome ranks assigned to a respective healthcare outcome in the respective community.
 4. The method of claim 1, assigning a population attribute rank to each of the population attributes, and representing one or more of the communities based on the ranking of one or more of the population attributes associated with the respective communities.
 5. The method of claim 1, comprising: computing a correlation between selected health care outcomes with selected population attributes; and identifying similar communities based on a result of the correlating.
 6. The method of claim 5, comprising: receiving an input that selects one or more of the population attributes and/or one or more of the health care outcomes; interactively computing the correlation in response to receiving the input.
 7. The method of claim 1 in which the plurality of health care outcomes include measures of health care cost, health care quality, and population health.
 8. The method of claim 1, in which the population attributes include measure of social capital, economics, demographics, health behaviors, health care provider capacity, health care provider incentives, integration and alignment of health care providers, and health information technology.
 9. A system for measuring health care related characteristics of a population segment, comprising: one or more electronic data storage systems coupled to one or more health care networks; a community health care database stored in the one or more electronic data storage systems, the community health care database including a first collection of health care data, the first collection including a plurality of health outcomes for health care consumers in a plurality of communities, wherein the health care outcomes for each consumer are associated with one or more of the communities; a second collection of community data stored in the one or more electronic data storage systems, the second collection including a plurality of population attributes that characterize the health care consumers in each of the communities; and one or more processors coupled in electronic communication with the one or more electronic data storage systems, the one or more processors configured for: receiving a first interactive input that selects one or more of the health care outcomes and/or one or more of the population attributes, identifying a correlation between the selected health care outcomes and one or more of the population attributes by accessing the first collection of health care data and the second collection of community data in response to receiving the interactive inputs, and representing the correlation to a user.
 10. The system of claim 9, wherein the one or more processors are configured to: assign a population attribute rank to each of the population attributes, and represent one or more of the communities based on the ranking of one or more of the population attributes associated with the respective communities.
 11. The system of claim 10, wherein the one or more processors are configured to: assign an outcome rank to each of the health care outcomes based on a predetermined hierarchy of outcomes to generate an enhanced community health care database; and represent one or more of the communities in association with one or more corresponding outcome ranks assigned to a respective healthcare outcome in the respective community.
 12. The method of claim 9, wherein the one or more processors are configured to identify similar communities based on a result of the correlating.
 13. The system of claim 9, wherein the one or more processors are configured to interactively compute the correlation in response to receiving the input.
 14. The system of claim 9, wherein the plurality of health care outcomes include measures of health care cost, health care quality, and population health.
 15. The system of claim 9, wherein the population attributes include measure of consisting of social capital, economics, demographics, health behaviors, health care provider capacity, health care provider incentives, integration and alignment of health care providers, and health information technology.
 16. A method of measuring community health care characteristics, comprising: receiving one or more health care outcomes for each of a plurality of communities; automatically scoring each of the communities based on the corresponding health care outcomes; and displaying a representation of the communities arranged based on their score.
 17. The method of claim 16, further comprising: receiving one or more population attributes that characterize the health care consumers in each of the plurality of communities; and displaying in indication of the corresponding population attributes in the representation of the communities.
 18. The method of claim 17, comprising: receiving an input that selects one or more of the population attributes and one or more of the health care outcomes; and interactively displaying a geographical representation of a relationship between the selected population attributes and the selected health care outcomes in response to receiving the input.
 19. The method of claim 17, comprising: receiving an input that selects a plurality of the population attributes; and interactively displaying a heat map representation of health care measures based on the selected population attributes for each of a number of communities in response to receiving the input.
 20. The method of claim 19, comprising: generating clustering information defining relationships between the displayed health care measures; and rearranging the heat map representation based on the clustering information.
 21. A method for measuring community health care attributes, comprising: storing a first collection of community data in the one or more electronic data storage systems, the first collection including a plurality attributes that characterize health care consumers in each of a plurality of communities; associating the attributes that characterize the health care consumers with one or more of the communities to generate a health care community population attribute database; identifying a correlation between a first one of the attributes in a community and a second one of the attributes in the community by accessing the health care community population attribute database; and representing the correlation to a user.
 22. A method for measuring community health care attributes, comprising: storing a first collection of health care data in one or more electronic storage systems, the first collection including a plurality of health outcomes for health care consumers in a plurality of communities; and associating the health care outcomes for each consumer with one or more of the communities to generate a community health care database.
 23. The method of claim 22, further comprising: identifying a correlation between a first one of the health care outcomes associated with a community and a second one of the health care outcomes associated with the community; and representing the correlation to a user.
 24. The method of claim 22, further comprising: and storing a second collection of community data in the one or more electronic data storage systems, the second collection including a plurality of population attributes that characterize the health care consumers in each of the communities; identifying a correlation between a first one of the population attributes and a second one of the population attributes; and representing the correlation to a user.
 25. The method of claim 22 wherein the plurality of health care outcomes are grouped into a plurality of categories, the categories comprising measures of health care cost, measures of health care quality, and measures of population health.
 26. The method of claim 22, further comprising statistically processing the plurality of health care outcomes to generate a plurality of categories of health care outcomes.
 27. The method of claim 22, wherein the population attributes are grouped into a plurality of categories, the categories comprising measure of social capital, measures of economics, measures of demographics, measures of health behaviors, measures of health care provider capacity, measures of health care provider incentives, measures of integration of health care providers, measures of alignment of health care providers, and measures of health information technology.
 28. The method of claim 22, further comprising statistically processing the plurality of population attributes to generate a plurality of categories of population attributes.
 29. The method of claim 22, further comprising: and storing a second collection of community data in the one or more electronic data storage systems, the second collection including a plurality of population attributes that characterize the health care consumers in each of the communities; identifying a correlation between one or more of the health care outcomes and one or more of the population attributes by accessing the first collection of health care data and the second collection of community data; and representing the correlation to a user.
 30. The method of claim 29, wherein the second collection further includes a plurality of population attributes that characterizes a system of healthcare, a healthcare infrastructure, and/or a healthcare environment in each of the communities.
 31. The method of claim 29, further comprising: assigning an outcome score to each of the health care outcomes based on a predetermined hierarchy of outcomes; and computing a composite outcome score for each community by statistically combining the outcome scores in each community respective community.
 32. The method of claim 31, further comprising: representing one or more of the communities based a respective composite outcome score of the respective communities.
 33. The method of claim 31, further comprising: assigning a population attribute score to each of the population attributes, and computing a composite population attribute score for each community by statistically combining the population attribute scores in each respective community.
 34. The method of claim 33, further comprising: representing one or more of the communities based on the composite population attribute score of the respective communities.
 35. The method of claim 33, further comprising: computing a community efficiency score by statistically comparing the composite population attribute score of the community with the composite outcome score of the community.
 36. The method of 33, further comprising: computing community efficiency scores for each of a plurality of communities by statistically comparing the composite population attribute score of each of the plurality of communities with the composite outcome score of each respective one of the plurality of communities; and displaying the community efficiency scores for each of the plurality of communities.
 37. The method of claim 33, comprising: displaying a geographical representation of a relationship between selected health outcomes and/or selected population attributes.
 38. The method of claim 33, comprising: displaying a heat map representation of health care measures based on selected health care outcomes and/or selected population attributes, the heat map representation including composite outcome score indication and/or a composite population attribute score indication for each of a plurality of the communities.
 39. The method of claim 38, comprising: computing clustering information that defines relationships between the displayed health care measures; and rearranging the heat map representation based on the clustering information. 